The compiler was never what you wanted
You have an orders topic on a Kafka cluster, its values encoded with Avro against a schema in the Schema Registry . You want the orders worth more than fifty euros on a topic of their own, and you have decided to do it with Kafka Streams — a JVM library, your code, your deployment. The schema has five fields: { "type" : "record" , "name" : "Order" , "namespace" : "com.alginte.demo" , "fields" : […
The story highlights the limitations of using Kafka Streams for complex data transformations. The author explains the seven steps required to set up and deploy a Kafka Streams application, including fetching the schema, generating code, configuring the build, writing the topology, packaging, deploying, and finally, filtering and transforming the data based on specific conditions.
The author then presents an alternative approach using Alginte, a browser-based topology builder that allows for a more interactive and dynamic way of defining and testing data transformations without the need for compiling and deploying the entire application each time. This alternative approach offers immediate feedback and eliminates the need for separate schema evolution handling, ultimately resulting in a tighter loop for development and testing.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
